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Load-frequency control01:28

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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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Machine-learning-aided method for optimizing beam selection and update period in 5G networks and beyond.

Ludwing Marenco1, Luiz E Hupalo2, Naylson F Andrade2

  • 1Instituto Nacional de Telecomunicações-INATEL, Santa Rita do Sapucaí, Brazil. ludwing@inatel.br.

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Summary
This summary is machine-generated.

This study introduces a machine learning approach to optimize beam pair selection and update times in 5G mmWave networks. The method significantly improves signal quality and data throughput by intelligently managing beam pair procedures.

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Area of Science:

  • Telecommunications Engineering
  • Machine Learning Applications
  • Wireless Communication Systems

Background:

  • 5G systems utilizing millimeter wave (mmWave) frequencies face challenges with time-intensive and resource-demanding beam pair selection and update procedures.
  • The efficiency of these procedures is critical for maintaining optimal performance in dynamic mobile environments.

Purpose of the Study:

  • To propose and evaluate a novel machine learning-based method for optimizing beam pair selection and update timing in 5G mmWave networks.
  • To enhance the efficiency and performance of beam management in wireless communication systems.

Main Methods:

  • Developing a machine learning model trained on collected beam pair data.
  • Implementing a three-module structure: spatial characterization of service areas, model training, and an optimization algorithm.
  • Utilizing user equipment's spatial position and velocity to compute optimal beam pair update times.

Main Results:

  • Observed improvements in Signal-to-Interference-plus-Noise Ratio (SINR) and throughput up to 15% in simulated mmWave scenarios.
  • Achieved a 20% reduction in beam pair selections.
  • Increased effective time between beam pair searches by approximately 10%.

Conclusions:

  • The proposed machine learning method offers real-time optimization for beam pair procedures in 5G and future wireless networks.
  • This intelligent approach addresses the limitations of traditional, resource-intensive beam management techniques.